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Predictive Control Based on Neuro-fuzzy Model for CSTR system

Zhang Yan-ya

Abstract

In this paper, a predictive control strategy based on neuro-fuzzy model is applied to Continuous Stirred Tank Reactor (CSTR) process, which has characteristic of highly nonlinearity. We use the neuro-fuzzy model to predict the behavior of the CSTR process over a certain prediction horizon, and an optimizer algorithm based on evolutionary programming to determine input sequence in a time window. Using the proposed method, the performance of PH tracking problem in a CSTR process is investigated. The result shows it can obtain satisfactory tracking effect.

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Available abstract

Key concepts: Continuous stirred-tank reactor, Computer science, Model predictive control, Control theory (sociology), Process (computing), Neuro-fuzzy, Nonlinear system, Sequence (biology)